CDC Pattern Implementer

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Implements Change Data Capture patterns for real-time data integration

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The instructions your AI receives, as published by a5c-ai/babysitter in library/specializations/data-engineering-analytics/skills/cdc-pattern-implementer/SKILL.md and read by ahel’s review.

Overview

Implements Change Data Capture patterns for real-time data integration. This skill provides expertise in CDC configuration and implementation across various database and streaming platforms.

Capabilities

Debezium connector configuration
CDC pattern selection (log-based, trigger-based, timestamp-based)
Initial snapshot strategy
Schema change handling
Exactly-once delivery configuration
Sink connector setup
Tombstone handling
CDC monitoring setup

Input Schema

{
  "sourceDatabase": {
    "type": "postgres|mysql|oracle|sqlserver",
    "connection": "object"
  },
  "tables": ["string"],
  "targetSystem": "kafka|kinesis|pubsub",
  "requirements": {
    "latencyMs": "number",
    "exactlyOnce": "boolean"
  }
}

Output Schema

{
  "connectorConfig": "object",
  "snapshotStrategy": "object",
  "schemaConfig": "object",
  "monitoringConfig": "object",
  "documentation": "string"
}

Target Processes

ETL/ELT Pipeline
Streaming Pipeline
Data Warehouse Setup

Usage Guidelines

Identify source database and tables for CDC
Define target streaming system
Specify latency and delivery guarantees
Configure appropriate snapshot strategy for initial load

Best Practices

Use log-based CDC when possible for minimal source impact
Plan initial snapshot strategy carefully for large tables
Implement proper error handling and dead letter queues
Monitor replication lag and connector health
Test schema evolution handling before production

Signals

GitHub stars: 2k
Forks: 112
Last commit: Sep 2026

Advanced

Item type: skill
Key: cdc-pattern-implementer
Source: github.com/a5c-ai/babysitter

github.com/a5c-ai/babysitter